Transcription of Gradient-Based Learning Applied to Document Recognition
{{id}} {{{paragraph}}}
Example: dental hygienist
Gradient-Based Learning Applied to Document Recognition
Gradient-Based Learning Appliedto Document RecognitionYANN LECUN,MEMBER, IEEE,L EON BOTTOU, YOSHUA BENGIO,ANDPATRICK HAFFNERI nvited PaperMultilayer neural networks trained with the back-propagationalgorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate networkarchitecture, Gradient-Based Learning algorithms can be usedto synthesize a complex decision surface that can classifyhigh-dimensional patterns, such as handwritten characters, withminimal preprocessing. This paper reviews various methodsapplied to handwritten character Recognition and compares themon a standard handwritten digit Recognition task. Convolutionalneural networks, which are specifically designed to deal withthe variability of two dimensional (2-D) shapes, are shown tooutperform all other Document Recognition systems are composed of multiplemodules including field extraction, segmentation, Recognition ,and language modeling.
with easily separable classes [1]. A combination of three factors has changed this vision over the last decade. First, the availability of low-cost machines with fast arithmetic units allows for reliance on more brute-force “numerical” methods than on algorithmic refinements. Second, the avail-
Loading..
Tags:
Information
Domain:
Source:
Link to this page:
Please notify us if you found a problem with this document: